Machine Learning Operation Engineer / Machine Learning Engineer

Role: Machine Learning Operation Engineer

Location: Pittsburgh, PA (Onsite)

Job Summary:

• As an MLOps Engineer at you will play a key role in bridging the gap between machine learning development and operational deployment. Your primary responsibilities will include designing, implementing, and optimizing end-to-end machine learning pipelines, ensuring seamless integration with existing systems.

Key Responsibilities:

• Deployment and Integration: Deploy machine learning models into production environments. Integrate machine learning algorithms with existing systems and applications.
• Infrastructure Management: Design, build, and maintain the infrastructure for machine learning operations. Collaborate with DevOps teams to ensure scalability, reliability, and security of ML systems.
• Automation: Implement automation tools and processes for model training, testing, and deployment. Streamline and optimize the end-to-end machine learning lifecycle.
• Monitoring and Troubleshooting: Set up monitoring systems to track the performance of deployed models. Troubleshoot issues related to data pipelines, model inference, and system performance.
• Collaboration: Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and ensure smooth integration. Collaborate with cross-functional teams to align machine learning workflows with business goals.
• Version Control: Implement version control for machine learning models and pipelines.
• Security and Compliance: Ensure the security and compliance of machine learning systems. Implement best practices for data privacy and protection.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Proven experience in deploying and managing machine learning models in production.
• Strong programming skills in one or more languages such as Python, Go, C, C++, Bash.
• Knowledge of cloud platforms (AWS, Azure, Google Cloud Platform) and their machine learning services.
• Familiarity with DevOps practices and tools.
• Understanding of continuous integration/continuous deployment (CI/CD) pipelines.
• Excellent problem-solving and communication skills.

Preferred Skills:

• Knowledge of machine learning frameworks (TensorFlow, PyTorch, JAX, XLA).
• Experience with orchestration tools (Airflow, Kubeflow).
• Familiarity with MLOps tools and platforms.
• Strong understanding of data engineering concepts.
• An ability to deploy and manage build systems that integrate a variety of languages and platforms, e.g., Bazel.
• Experience with containerization technologies (e.g., Docker, Kubernetes).
• Experience developing custom with machine learning benchmarking systems on platforms such as TensorBoard, Weights & Biases, neptune.ai, etc.
• Familiarity with machine learning deployment tools such as TFLite, ONNX, TensorRT, and ML Kit.